Neural Network-Based Super-Twisting Algorithm for Controlling a Floating Offshore Wind Turbine in Low-Wind Region

This paper studies a hybrid control strategy for enhancing power generation from floating offshore wind turbines (FOWTs) in low-wind conditions. To handle the uncertain dynamics of the FOWT a super-twisting (STW) control strategy is proposed. Later, to improve the performance of the STW control and to reduce the control effort, the STW control and a radial basis function neural network (RBFNN) are integrated to approximate the uncertainties and unmodeled dynamics. Using the OpenFAST simulator, we compare the strategy’s effectiveness with and without neural network integration.